Weather API

A Python script to visualize the weather of 500+ cities across the world of varying distance from the equator. To accomplish this, I utilized a simple Python library, the OpenWeatherMap API, and a little common sense to create a representative model of weather across world cities.

Dependencies

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import requests
import time
import json
import scipy.stats as st
from scipy.stats import linregress
from api_keys import weather_api_key

from citipy import citipy

Generating City Lists

lat_lngs = []
cities = []

# A set of random lat and lng combinations
lats = np.random.uniform(lat_range[0], lat_range[1], size=1500)
lngs = np.random.uniform(lng_range[0], lng_range[1], size=1500)
lat_lngs = zip(lats, lngs)

# Identifying nearest city for each lat, lng combination
for lat_lng in lat_lngs:
    city = citipy.nearest_city(lat_lng[0], lat_lng[1]).city_name
    
    # If the city is unique, then add it to a our cities list
    if city not in cities:
        cities.append(city)

# Print the city count to confirm sufficient count
len(cities)

Performing API Calls and inserting cities into a DataFrame

url = "http://api.openweathermap.org/data/2.5/weather?"
units = "imperial"

print("Beginning Data Retrieval")
print("------------------------")

record_num = 1
num_of_set = 1

for index, row in cities_weather_df.iterrows():
    
    city = row["City"]
        
    target_url = f"{url}appid={weather_api_key}&units={units}&q={city}"
    response = requests.get(target_url)
    data = response.json()
    
    try:
        cities_weather_df.loc[index, "Cloudiness"] = data["clouds"]["all"]
        cities_weather_df.loc[index, "Country"] = data["sys"]["country"]
        cities_weather_df.loc[index, "Date"] = data["dt"]
        cities_weather_df.loc[index, "Humidity"] = data["main"]["humidity"]
        cities_weather_df.loc[index, "Lat"] = data["coord"]["lat"]
        cities_weather_df.loc[index, "Lng"] = data["coord"]["lon"]
        cities_weather_df.loc[index, "Max Temp"] = data["main"]["temp_max"]
        cities_weather_df.loc[index, "Wind Speed"] = data["wind"]["speed"]
            
        print(f"Processing Record {record_num} of Set {num_of_set}| {city}")
        record_num += 1
        
    except:
        print("City not found. Skipping...")
        
    if record_num % 51 == 0 and record_num >= 51:
            num_of_set += 1
            record_num = 1
            time.sleep(60)

print("-----------------------")
print("Data Retrieval Complete")
print("-----------------------")

I used the following scatter plots to visualize the data:

• Temperature (F) vs. Latitude
• Humidity (%) vs. Latitude
• Cloudiness (%) vs. Latitude
• Wind Speed (mph) vs. Latitude

Scatter plot code snippet for Temperature (F) vs. Latitude

latitude_values = cities_df["Lat"]
temp_values = cities_df["Max Temp"]
plt.figure(figsize=(10,5))
plt.scatter(latitude_values, temp_values, marker="o", facecolors="blue", alpha=0.6, edgecolor="black", s=50)
plt.title("City Latitude vs. Max Temperature (5/13/2021)")
plt.xlabel("Latitude")
plt.ylabel("Max Temperature (F)")
plt.grid()
plt.show()
plt.tight_layout()

Scatter plot images for Temperature (F), Humidity (%), Cloudiness (%) and Wind Speed (mph) vs. Latitude

I also ran linear regression on each relationship above by creating 8 additional plots. I separated the plots into Northern Hemisphere (greater than or equal to 0 degrees latitude) and Southern Hemisphere (less than 0 degrees latitude). The following are the plots are created (and also calculated the r-value of each):

• Northern Hemisphere - Temperature (F) vs. Latitude
• Southern Hemisphere - Temperature (F) vs. Latitude
• Northern Hemisphere - Humidity (%) vs. Latitude
• Southern Hemisphere - Humidity (%) vs. Latitude
• Northern Hemisphere - Cloudiness (%) vs. Latitude
• Southern Hemisphere - Cloudiness (%) vs. Latitude
• Northern Hemisphere - Wind Speed (mph) vs. Latitude
• Southern Hemisphere - Wind Speed (mph) vs. Latitude

Code snippet for linear regression

northern_hemisphere = cities_df["Lat"] > 0
northern_hemisphere_table = cities_df[northern_hemisphere]

x_values = northern_hemisphere_table["Lat"]
y_values = northern_hemisphere_table["Max Temp"]
(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)
regress_values = x_values * slope + intercept
line_eq = "y = " + str(round(slope,2)) + "x + " + str(round(intercept,2))

# print(f"The r-value is {st.pearsonr(x_values,y_values)[0]}")
print(f"The r-value is: {rvalue}")

plt.figure(figsize=(10,5))
plt.scatter(x_values,y_values, marker="o", facecolors="blue", alpha=0.6, edgecolor="black", s=50)
plt.plot(x_values,regress_values,"r-", alpha=0.6)
plt.annotate(line_eq,(0,20),fontsize=12,color="red")
plt.title("Northern Hemisphere - Max Temperature vs. Latitude")
plt.xlabel("Latitude")
plt.ylabel("Max Temperature (F)")
plt.show()

Vacation API

I used the data i gathered above, as well as jupyter-gmaps and the Google Places API to plan likely vacation spots. I was able to narrow down the data frame to only show cities with perfect weather conditions: A max temperature lower than 80 degrees but higher than 70; Wind speed less than 10 mph; and Zero cloudiness.

Creating Humidity HeatMap

gmaps.configure(g_key)
locations = cities_weather_df[["Lat", "Lng"]]
humidity = cities_weather_df["Humidity"].astype(float)
max_humidity = max(humidity)
fig = gmaps.figure()
heat_layer = gmaps.heatmap_layer(locations, weights=humidity, 
                                 dissipating=False, max_intensity=max_humidity, 
                                 point_radius=3.5)
fig.add_layer(heat_layer)
fig 

Creating new DataFrame fitting weather criteria (Perfect temperature between 70 and 80 Degrees, WindSpeed less tha 10mph and Zero Cloudiness

perfect_conditions = cities_weather_df[(cities_weather_df["Max Temp"] >70) 
                                       & (cities_weather_df["Max Temp"] < 80) 
                                       & (cities_weather_df["Wind Speed"] < 10) 
                                       & (cities_weather_df["Cloudiness"] == 0)]